AI Infrastructure and Platform Architect

Position Summary

• We are seeking an experienced AI Infrastructure and
• Platform Architect to design, optimize, and manage scalable AI
• infrastructure and platforms across on-premises, cloud, and hybrid
• environments.
• The ideal candidate should have strong experience with
• GPU-based systems, AI/ML platforms, infrastructure architecture, performance
• optimization, capacity planning, and production support. The role will work
• closely with AI/ML developers, DevOps engineers, data engineers, and solution
• architects to improve the performance, reliability, scalability, and cost
• efficiency of AI solutions.

Key Responsibilities

Design

and manage AI infrastructure for model training, fine-tuning, inference,

computer vision, Generative AI, and LLM workloads.

Define

CPU, GPU, RAM, VRAM, storage, networking, cooling, and power requirements.

Review

existing hardware and platform performance and recommend upgrades or

optimizations.

Perform

capacity planning to support future workloads and minimize frequent

hardware changes.

Build

and maintain AI platforms using Linux, Docker, Kubernetes, GPU

orchestration, and cloud services.

Configure

and manage NVIDIA drivers, CUDA, cuDNN, TensorRT, and related AI

acceleration technologies.

Monitor

system health, GPU utilization, memory usage, storage performance, and

network throughput.

Diagnose

infrastructure failures, system crashes, performance bottlenecks, and

platform outages.

Implement

monitoring, alerting, backup, disaster recovery, security, and operational

best practices.

Prepare

architecture documents, hardware specifications, technical

recommendations, and operational runbooks.

Support

production deployment, troubleshooting, and continuous platform

improvement.

AI Solution Optimization

The candidate should also be capable of

Reviewing

the end-to-end AI solution and identifying performance, architecture, and

infrastructure gaps.

Recommending

improvements to scalability, reliability, maintainability, and cost

efficiency.

Supporting

AI/ML developers with model training and experimentation environments.

Helping

reduce training time through GPU optimization, distributed training,

resource tuning, and efficient data pipelines.

Providing

guidance on model accuracy, evaluation, hyperparameter tuning, and

experimentation practices.

Improving

model-serving and inference performance.

Mentoring

existing team members on AI infrastructure and production-readiness best

practices.

Required Skills

AI

infrastructure and GPU-based computing

NVIDIA

GPU architecture, CUDA, cuDNN, NCCL, and TensorRT

Linux

administration

Docker

• and Kubernetes
• PyTorch,
• TensorFlow, Hugging Face, or similar frameworks

Cloud

and on-premises AI platforms

Infrastructure

sizing and capacity planning

Performance

monitoring and troubleshooting

High-performance

• storage and networking
• MLOps,
• CI/CD, automation, and Infrastructure as Code

Monitoring

tools such as Prometheus, Grafana, NVIDIA DCGM, or OpenTelemetry

Bachelor's

or Master's degree in Computer Science, Artificial Intelligence,

Information Technology, Engineering, or a related field.

Strong

overall experience in infrastructure, cloud, platform engineering,

architecture, or AI systems.

A

• minimum of 3 years of direct, hands-on experience specifically working
• with AI infrastructure, machine learning platforms, GPU environments, or
• production AI workloads.

Proven

experience designing, deploying, supporting, or optimizing production AI

systems.

Strong

• problem-solving, troubleshooting, communication, and technical
• documentation skills.
• The candidate's total professional experience may be
• significantly higher. However, at least three years should involve genuine,
• relevant, hands-on work with AI systems and platforms.

Added Advantage

Preference will be given to candidates who have experience

with:

AI

solution architecture

Large

Language Models and Generative AI

RAG

and agentic AI systems

Distributed

model training

Computer

vision and edge AI

Model-serving

platforms

AI

performance benchmarking

FinOps

and infrastructure cost optimization

High

Performance Computing environments

Experience Validation

Candidates should be able to explain their direct

contribution to AI projects, including:

AI

infrastructure or platforms they designed or managed

GPU

and hardware-sizing decisions

Model

training or inference environments supported

Performance

issues diagnosed and resolved

Improvements

achieved in training time, utilization, reliability, or cost

Production

• AI workloads they deployed or maintained
• General DevOps, cloud, or system administration experience
• without direct AI or machine learning exposure will not be sufficient for this
• position.